Upload evaluate.py

evaluate.py

@@ -0,0 +1,141 @@
+import os
+# os.environ['CUDA_VISIBLE_DEVICES'] = '3'
+
+import pathlib
+from CoT.task import CoT_Task
+from ToT.task import ToT_Task
+from MCTS.task import MCTS_Task
+import argparse
+from utils.visualize import visualize
+from utils.json_operator import *
+from utils.verify_answer import *
+from utils.self_consistency import get_consistency_output_scibench
+import copy
+
+def run(arguments):
+    print('-'*30, 'Begin testing', '-'*30, '\n')
+    # file = f'data/{arguments.task_name}/{arguments.file}.json'
+    # file = "/cpfs/29f69eb5e2e60f26/code/sft_intern/lh/slz/ReST-MCTS/data/math/math_500.json"
+    file = arguments.load_file_path
+    print('** file_path: ', file)
+    try:
+        data_list = load_file(file)
+        data_len = len(data_list)
+    except Exception as e:
+        print(f'File must be standardized json!\nError type:{e}\n')
+        return
+    assert data_len > 0, "Data list is empty!\n"
+    # assert 'content' in data_list[0].keys() and 'answer' in data_list[0].keys(), "Key error, Make sure json object contain correct keys!\n"
+
+    output_list = []
+    correct_count = 0
+    for i in range(data_len):
+    # for i in range(0, 1):
+        print(f'Begin to solve the problem {i+1}...\n')
+        # data = data_list[i]['question']
+        # answer = data_list[i]['answer'][0]
+        data = data_list[i]['question']
+        answer = data_list[i]['solution']
+        if arguments.mode == 'cot':
+            Task = CoT_Task(data, arguments.propose_method, arguments.value_method, arguments.temperature, evaluate=arguments.evaluate)
+            if arguments.consistency:
+                outputs = []
+                for cnt in range(3):
+                    output = Task.run()
+                    outputs.append(output)
+                output = get_consistency_output_scibench(outputs)
+            else:
+                output = Task.run()
+
+        elif arguments.mode == 'tot':
+            Task = ToT_Task(data, arguments.propose_method, arguments.value_method, arguments.algorithm,
+                            arguments.branch, arguments.select_branch, arguments.max_depth, arguments.end_gate,
+                            arguments.select_method, arguments.temperature, use_case_prompt=arguments.use_case_prompt,
+                            low=arguments.low, high=arguments.high, evaluate=arguments.evaluate)
+            output, root = Task.run()
+            if arguments.visualize:
+                visualize(root, Task, arguments.task_name, arguments.file, i + 1)
+        else:
+            Task = MCTS_Task(data, arguments.propose_method, arguments.value_method, arguments.branch, arguments.end_gate,
+                             arguments.roll_policy, arguments.roll_branch, arguments.roll_forward_steps, arguments.time_limit,
+                             arguments.iteration_limit, arguments.exploration_constant, arguments.alpha, arguments.inf,
+                             arguments.temperature, use_case_prompt=arguments.use_case_prompt, use_reflection=arguments.use_reflection,
+                             low=arguments.low, high=arguments.high, evaluate=arguments.evaluate, answer=answer, lang='en')
+            output, root = Task.run()
+
+            if arguments.visualize:
+                visualize(root, Task, arguments.task_name, arguments.file, i + 1)
+
+        # evaluate metrics
+        if arguments.evaluate:
+            print('** output: ', output)
+            result = verify_float(answer, output['summary'])
+            output.update({'answer': answer, 'accurate': result})
+            if result:
+                print(f'The answer of problem {i+1} is correct.\n')
+                correct_count += 1
+            else:
+                print(f'The answer of problem {i+1} is wrong.\n')
+        print(f'The solution to problem {i+1} is complete.\n')
+
+        # output
+        base_dir = os.getcwd()
+        output_dir = pathlib.Path(f'{base_dir}/outputs/{arguments.task_name}/{arguments.file}/{Task.mode}')
+        output_file = f'{base_dir}/outputs/{arguments.task_name}/{arguments.file}/{Task.mode}/{Task.propose_method}_{Task.value_method}_{arguments.save_name}.json'
+        
+        data_item = copy.deepcopy(data_list[i])  # 创建深拷贝
+        data_item['mcts_output'] = output
+
+        output_list.append(data_item)
+        pathlib.Path.mkdir(output_dir, exist_ok=True, parents=True)
+        dump_json(output_file, output_list)
+        print('** output_file: ', output_file)
+        
+
+    print('_' * 60)
+    # accuracy
+    if args.evaluate:
+        print(f'Test accuracy:{correct_count / data_len}\n')
+        print(f'Correct number of problems:{correct_count}\nTotal number of questions:{data_len}\n')
+    print('_' * 60)
+
+
+def parse_args():
+    base_args = argparse.ArgumentParser()
+    base_args.add_argument('--load_file_path', type=str, default='scibench')
+    base_args.add_argument('--task_name', type=str, default='scibench')
+    base_args.add_argument('--file', type=str, default='thermo_standardized')  # json
+    base_args.add_argument('--save_name', type=str, default='test')  # json
+    base_args.add_argument('--propose_method', type=str, choices=['gpt', 'glm', 'llama', 'local'], default='glm')
+    base_args.add_argument('--value_method', type=str, choices=['gpt', 'glm', 'local'], default='local')
+    base_args.add_argument('--mode', type=str, choices=['cot', 'tot', 'mcts'], default='tot')
+    base_args.add_argument('--temperature', type=float, default=0.7)
+    base_args.add_argument('--time_limit', type=int, default=None)
+    base_args.add_argument('--iteration_limit', type=int, default=100)
+    base_args.add_argument('--roll_policy', type=str, choices=['random', 'greedy'], default='greedy')
+    base_args.add_argument('--exploration_constant', type=float, default=0.4)
+    base_args.add_argument('--roll_forward_steps', type=int, default=2)
+    base_args.add_argument('--end_gate', type=float, default=0.9)  # End threshold
+    base_args.add_argument('--branch', type=int, default=3)
+    base_args.add_argument('--roll_branch', type=int, default=1)
+    base_args.add_argument('--inf', type=float, default=0.8)
+    base_args.add_argument('--evaluate', type=str, default='scibench')  # Whether to evaluate (empty means no evaluation)
+    base_args.add_argument('--alpha', type=float, default=0.5)
+    base_args.add_argument('--visualize', type=bool, default=False)  # visualization
+    base_args.add_argument('--use_case_prompt', type=bool, default=False)  # Use sample prompts
+    base_args.add_argument('--use_reflection', type=str, choices=['simple', 'common'], default='simple')  # Use reflective mode
+    base_args.add_argument('--low', type=float, default=0)
+    base_args.add_argument('--high', type=float, default=1)
+    base_args.add_argument('--algorithm', type=str, choices=['dfs', 'bfs'], default='dfs')
+    base_args.add_argument('--select_branch', type=int, default=2)
+    base_args.add_argument('--max_depth', type=int, default=8)
+    base_args.add_argument('--select_method', type=str, choices=['greedy', 'sample'], default='greedy')
+    base_args.add_argument('--consistency', type=bool, default=True)
+
+    arguments = base_args.parse_args()
+    return arguments
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    run(args)